This is a personal Rmarkdown document I have created to visualize the COVID-19 updates and some preliminary exploratory data analysis (EDA). The source of this data is the github repository created and maintained by the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU).
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(forecast))
suppressPackageStartupMessages(library(zoo))
suppressPackageStartupMessages(library(xts))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(gghighlight))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(directlabels))
suppressPackageStartupMessages(library(scales))
suppressPackageStartupMessages(library(plotly))
#suppressPackageStartupMessages(library(rjson))
COVID_confirmed_global_raw <- read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
COVID_deaths_global_raw <- read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
COVID_recovered_global_raw <- read_csv("csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
# Reshape to longer format
COVID_confirmed_global_longer <- COVID_confirmed_global_raw %>%
pivot_longer(cols = c('1/22/20':names(COVID_confirmed_global_raw)[ncol(COVID_confirmed_global_raw)]),
names_to = "date",
values_to = "n_cases")
COVID_deaths_global_longer <- COVID_deaths_global_raw %>%
pivot_longer(cols = c('1/22/20':names(COVID_deaths_global_raw)[ncol(COVID_deaths_global_raw)]),
names_to = "date",
values_to = "n_cases")
COVID_recovered_global_longer <- COVID_recovered_global_raw %>%
pivot_longer(cols = c('1/22/20':names(COVID_recovered_global_raw)[ncol(COVID_recovered_global_raw)]),
names_to = "date",
values_to = "n_cases")
# change column names
colnames(COVID_confirmed_global_longer) <- c('state', 'country', 'lat', 'long','date', 'n_cases')
colnames(COVID_deaths_global_longer) <- c('state', 'country', 'lat', 'long','date', 'n_cases')
colnames(COVID_recovered_global_longer) <- c('state', 'country', 'lat', 'long','date', 'n_cases')
# drop `state` column and create a `new_cases` column
COVID_confirmed_global_longer <- COVID_confirmed_global_longer %>%
select(-state)%>%
group_by(country, date) %>%
summarize(n_cases = sum(n_cases))
COVID_deaths_global_longer <- COVID_deaths_global_longer %>%
select(-state)%>%
group_by(country, date) %>%
summarize(n_cases = sum(n_cases))
COVID_recovered_global_longer <- COVID_recovered_global_longer %>%
select(-state) %>%
group_by(country, date) %>%
summarize(n_cases = sum(n_cases))
# convert date columns from character to date format
COVID_confirmed_global_longer$date <- as.Date(COVID_confirmed_global_longer$date, format = '%m/%d/%Y')
COVID_deaths_global_longer$date <- as.Date(COVID_deaths_global_longer$date, format = '%m/%d/%Y')
COVID_recovered_global_longer$date <- as.Date(COVID_recovered_global_longer$date, format = '%m/%d/%Y')
COVID_confirmed_global_longer <- COVID_confirmed_global_longer %>%
arrange(country, date) %>%
mutate(new_cases = n_cases-lag(n_cases, default = 0))
COVID_deaths_global_longer <- COVID_deaths_global_longer %>%
arrange(country, date) %>%
mutate(new_cases = n_cases-lag(n_cases, default = 0))
COVID_recovered_global_longer <- COVID_recovered_global_longer %>%
arrange(country, date) %>%
mutate(new_cases = n_cases-lag(n_cases, default = 0))
Let’s look at the current data format
knitr::kable(head(COVID_confirmed_global_longer),format = 'markdown')
| country | date | n_cases | new_cases |
|---|---|---|---|
| Afghanistan | 0020-01-22 | 0 | 0 |
| Afghanistan | 0020-01-23 | 0 | 0 |
| Afghanistan | 0020-01-24 | 0 | 0 |
| Afghanistan | 0020-01-25 | 0 | 0 |
| Afghanistan | 0020-01-26 | 0 | 0 |
| Afghanistan | 0020-01-27 | 0 | 0 |
world_summary <- function() {
df1 <- COVID_confirmed_global_longer %>%
group_by(country) %>%
summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases)) %>%
ungroup() %>%
summarize(n_cases_total = sum(n_cases_today),
new_cases_total = sum(new_cases_today))
df2 <- COVID_deaths_global_longer %>%
group_by(country) %>%
summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases)) %>%
ungroup() %>%
summarize(n_cases_total = sum(n_cases_today),
new_cases_total = sum(new_cases_today))
df3 <- COVID_recovered_global_longer %>%
group_by(country) %>%
summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases)) %>%
ungroup() %>%
summarize(n_cases_total = sum(n_cases_today),
new_cases_total = sum(new_cases_today))
print(paste0("number of total confirmed cases in the world as of today: ", df1$n_cases_total, " with ", df1$new_cases_total, " new cases"))
print(paste0("number of total deaths in the world as of today: ", df2$n_cases_total, " with ", df2$new_cases_total, " new deaths"))
print(paste0("number of total recovered cases in the world as of today: ", df3$n_cases_total, " with ", df3$new_cases_total, " new cases"))
}
country_summary <- function(country1) {
df1 <- COVID_confirmed_global_longer %>% group_by(country) %>% dplyr::filter(country==country1) %>% summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases))
df2 <- COVID_deaths_global_longer %>% group_by(country)%>% dplyr::filter(country==country1) %>% summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases))
df3 <- COVID_recovered_global_longer %>% group_by(country)%>% dplyr::filter(country==country1) %>% summarize(n_cases_today = max(n_cases),
new_cases_today = dplyr::last(new_cases))
#
print(paste0("number of confirmed cases in ", country1, " as of today: ", df1$n_cases_today, " with ", df1$new_cases_today, " new cases"))
# df1$n_cases_today
print(paste0("number of deaths in ", country1, " as of today: ", df2$n_cases_today, " with ", df2$new_cases_today, " new deaths"))
# df2$n_cases_today
print(paste0("number of recovered cases in ", country1, " as of today: ", df3$n_cases_today, " with ", df3$new_cases_today, " new cases"))
# df3$n_cases_today
}
world_summary()
## [1] "number of total confirmed cases in the world as of today: 932606 with 75118 new cases"
## [1] "number of total deaths in the world as of today: 46812 with 4702 new deaths"
## [1] "number of total recovered cases in the world as of today: 193471 with 15143 new cases"
country_summary("US")
## [1] "number of confirmed cases in US as of today: 213372 with 25200 new cases"
## [1] "number of deaths in US as of today: 4757 with 884 new deaths"
## [1] "number of recovered cases in US as of today: 8474 with 1450 new cases"
country_summary("Italy")
## [1] "number of confirmed cases in Italy as of today: 110574 with 4782 new cases"
## [1] "number of deaths in Italy as of today: 13155 with 727 new deaths"
## [1] "number of recovered cases in Italy as of today: 16847 with 1118 new cases"
country_summary("Spain")
## [1] "number of confirmed cases in Spain as of today: 104118 with 8195 new cases"
## [1] "number of deaths in Spain as of today: 9387 with 923 new deaths"
## [1] "number of recovered cases in Spain as of today: 22647 with 3388 new cases"
country_summary("China")
## [1] "number of confirmed cases in China as of today: 82361 with 82 new cases"
## [1] "number of deaths in China as of today: 3316 with 7 new deaths"
## [1] "number of recovered cases in China as of today: 76405 with 199 new cases"
country_summary("Egypt")
## [1] "number of confirmed cases in Egypt as of today: 779 with 69 new cases"
## [1] "number of deaths in Egypt as of today: 52 with 6 new deaths"
## [1] "number of recovered cases in Egypt as of today: 179 with 22 new cases"
# Get Manufacturer
df <- COVID_confirmed_global_longer %>% mutate(country_sum = ifelse(n_cases > 5000, country,"other"))
df <- df %>% group_by(country_sum)
df <- df %>% summarize(count = max(n_cases))
fig <- df %>% plot_ly(labels = ~country_sum, values = ~count, text = ~country_sum)
fig <- fig %>% add_pie(hole = 0.4)
fig <- fig %>% layout(title = "Confirmed cases worldwide", showlegend = F,
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig
# One trace (more performant, but less interactive)
COVID_confirmed_global_longer %>%
group_by(country) %>%
plot_ly(x = ~date, y = ~n_cases, color = ~country) %>%
add_bars(text = ~country)%>%
layout(barmode = "stack",
showlegend = FALSE)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
plot_countries <- function(df, curve_title, cumulative, ...) {
df1 <- df %>%
dplyr::filter(country %in% list(...))
if (cumulative) {
p1 = ggplot(df1, aes(date, n_cases, group=country, color=country))+
geom_line()+
scale_x_date(date_breaks = "3 days")+
scale_y_log10(labels = function(x) format(x, scientific = FALSE),
name = "number of cases",
breaks = scales::breaks_log(n = 10))+
theme_bw()+
theme(axis.text.x = element_text(angle = 90), legend.position = "none")+
ggtitle(curve_title)+
geom_dl(data = df1, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))
} else{
p1 = ggplot(df1, aes(date, new_cases, group=country, color=country))+
geom_line()+
scale_x_date(date_breaks = "3 days")+
scale_y_log10(labels = function(x) format(x, scientific = FALSE),
name = "number of cases",
breaks = scales::breaks_log(n = 10))+
theme_bw()+
theme(axis.text.x = element_text(angle = 90), legend.position = "none")+
ggtitle(curve_title)+
geom_dl(data = df1, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))
}
return(p1)
}
plot_countries(COVID_confirmed_global_longer, curve_title = "Confirmed cases (cummulative)", cumulative = TRUE,"china", "US", "Italy", "Canada", "Egypt")
plot_countries(COVID_deaths_global_longer, curve_title = "Death cases (cummulative)", cumulative = TRUE, "china","US", "Italy", "Canada", "Egypt")
plot_countries(COVID_recovered_global_longer, curve_title = "Recovered cases (cummulative)",cumulative = TRUE, "china","US", "Italy", "Canada", "Egypt")
plot_countries(COVID_confirmed_global_longer, curve_title = "New confirmed cases", cumulative = FALSE, "china","US", "Italy", "Canada", "Egypt")
plot_countries(COVID_deaths_global_longer, curve_title = "New death cases", cumulative = FALSE, "china","US", "Italy", "Canada", "Egypt")
plot_countries(COVID_recovered_global_longer, curve_title = "New recovered cases", cumulative = FALSE, "china","US", "Italy", "Canada", "Egypt")
Inspired by this minuteearth video. The thing about this visualization is that it doesn’t plot the Cumulative number of confirmed cases with time, instead with the number of new cases on a log-scale, which is more intuitive. Multiple comparisons between countries with very different number of cases could be very made very clear, and it is very easy to detect whether things are getting better.
COVID_confirmed_smoothed <- COVID_confirmed_global_longer %>%
tidyr::nest(-country) %>%
dplyr::mutate(m = purrr::map(data, loess,
formula = new_cases ~ n_cases, span = 0.6),
fitted = purrr::map(m, `[[`, "fitted"))
COVID_confirmed_smoothed <- COVID_confirmed_smoothed %>%
dplyr::select(-m) %>%
tidyr::unnest()
COVID_confirmed_smoothed2 <- COVID_confirmed_smoothed %>%
dplyr::filter(country %in% c("US", "China", "Italy", "Korea, South", "Iran", "Egypt"))
ggplot(data = COVID_confirmed_smoothed2, aes(n_cases, fitted))+
geom_path(data = COVID_confirmed_smoothed2,aes(n_cases,fitted,color = country, group = country))+
theme_bw()+
ylab("number of cases")+
scale_y_log10(labels = function(x) format(x, scientific = FALSE))+
scale_x_log10(labels = function(x) format(x, scientific = FALSE))+
geom_dl(data = COVID_confirmed_smoothed2, aes(label = country), method = list(dl.combine("first.points", "last.points"), cex = 0.8))+
xlab(label = "Total confirmed cases")+
ylab(label = "number of new cases")+
theme(legend.position="none")